Next Article in Journal
Linking Land Cover Change with Landscape Pattern Dynamics Induced by Damming in a Small Watershed
Next Article in Special Issue
A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies
Previous Article in Journal
2D Phase-Based RFID Localization for On-Site Landslide Monitoring
Previous Article in Special Issue
Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image
 
 
Article

Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore

by 1, 1, 2 and 1,*
1
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
2
ETH Zurich, Future Resilient Systems, Singapore-ETH Centre, Singapore 138602, Singapore
*
Author to whom correspondence should be addressed.
Academic Editors: Gemine Vivone and Liang-Jian Deng
Remote Sens. 2022, 14(15), 3579; https://doi.org/10.3390/rs14153579
Received: 25 June 2022 / Revised: 18 July 2022 / Accepted: 19 July 2022 / Published: 26 July 2022
(This article belongs to the Special Issue Machine Vision and Advanced Image Processing in Remote Sensing)
Selection of store sites is a common but challenging task in business practices. Picking the most desirable location for a future store is crucial for attracting customers and becoming profitable. The classic multi-criteria decision-making framework for store site selection oversimplifies the local characteristics that are both high dimensional and unstructured. Recent advances in deep learning enable more powerful data-driven approaches for site selection, many of which, however, overlook the interaction between different locations on the map. To better incorporate the spatial interaction patterns in understanding neighborhood characteristics and their impact on store placement, we propose to learn a graph convolutional network (GCN) for highly effective site selection tasks. Furthermore, we present a novel dataset that encompasses land use information as well as public transport networks in Singapore as a case study to benchmark site selection algorithms. It allows us to construct a geospatial GCN based on the public transport system to predict the attractiveness of different store sites within neighborhoods. We show that the proposed GCN model outperforms the competing methods that are learning from local geographical characteristics only. The proposed case study corroborates the geospatial interactions and offers new insights for solving various geographic and transport problems using graph neural networks. View Full-Text
Keywords: site selection; graph convolutional networks; spatial prediction; geographic information; transportation site selection; graph convolutional networks; spatial prediction; geographic information; transportation
Show Figures

Graphical abstract

MDPI and ACS Style

Lan, T.; Cheng, H.; Wang, Y.; Wen, B. Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore. Remote Sens. 2022, 14, 3579. https://doi.org/10.3390/rs14153579

AMA Style

Lan T, Cheng H, Wang Y, Wen B. Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore. Remote Sensing. 2022; 14(15):3579. https://doi.org/10.3390/rs14153579

Chicago/Turabian Style

Lan, Tian, Hao Cheng, Yi Wang, and Bihan Wen. 2022. "Site Selection via Learning Graph Convolutional Neural Networks: A Case Study of Singapore" Remote Sensing 14, no. 15: 3579. https://doi.org/10.3390/rs14153579

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop